Quantum annealing (QA) attempts to exploit quantum fluctuations to solve computational problems faster than it is possible with classical computers. As an approach designed to solve optimization problems, QA is a special case of adiabatic quantum computation (AQC), a universal model of quantum computing. In AQC, a system is designed to follow the instantaneous ground state of a time-dependent Hamiltonian whose final ground state encodes the solution to the problem of interest. This results in a certain amount of stability, since the system can thermally relax to the ground state after an error, as well as resilience to errors, since the presence of a finite energy gap suppresses thermal and dynamical excitations.
Despite this inherent robustness to certain forms of noise, AQC requires error-correction to ensure scalability, just like any other form of quantum information processing. Various error correction proposals for AQC and QA have been made, but an accuracy-threshold theorem for AQC is not yet known, unlike in the circuit model. A direct AQC simulation of a fault-tolerant quantum circuit leads to many-body (high-weight) operators that are difficult to implement or to a myriad of other problems. Nevertheless, a scalable method to reduce the effective temperature would go a long way towards approaching the ideal of closed-system AQC, where quantum speedups are known to be possible.